Best Practice Center
CustomerTool's first Web service is a data mining application that uses association rules to help your company affordably answer the following questions:
- What are the most effective cross-selling strategies?
- What are my market basket differentials?
- Who are my best and worst customers?
- Which products should and shouldn't be promoted to which customers?
- How did a test product perform in a basket of goods?
Some people call them “Related Selling” analyses. Others call them “Market Basket” analyses. Regardless of the name, association rules are used to examine the relationships that exist between the products you sell and the customers that buy them. These rules are typically expressed as probabilities, percents and likelihoods. For example, 10% of my customers buy Jewelry and Handbags, 50% of the customers who buy Denim also buy Polos, or Denim customers are 3.5 times more likely to buy Sunglasses than any of my other customers. The goal of the analysis is identify the co-occurrences of different products that happen with the greatest frequency. By performing this analysis you will be able to answer the questions posed above.
CustomerTool's association rules application analyzes POS transaction data to find the associations between the products your customers purchase. You do not need to be a statistician to use CutomerTool. All you need is access to the data. There is no hardware or special software to buy. Just upload your data and start getting answers.
Association Rules: An Example
CustomerTool produces 4 association rules: Expected Confidence, Support, Confidence and Lift. Each of these rules has a specific use in evaluating selling data. See the example below for a quick description of these rules.
Expected Confidence Is the probability that any product you sell will be purchased by a customer. For example, a store had 100 customers today, and 20 of them purchased at least one shirt. Consequently, there is a 20% chance (EC) that any customer transaction will include a shirt.
Support Is the probability that any combination of products you sell will be purchased by a customer. If 10 of the 20 customers who bought a shirt also bought a hat, then shirts and hats have a 10% chance (Support) of occurring together on any transaction.
Confidence Is the probability that if a customer buys a product(s) they will also buy another. Using our example above, shirt customers have a 50% chance of also buying a hat (10/20).
Lift Is expressed as a ratio and measures the likelihood of buying one product given that another product has been purchased. Lift is calculated by dividing Confidence by Expected Confidence. In our example, someone who buys a shirt is 2.5 times more likely to buy a hat than someone just walking in the store. NOTE: It is possible to uncover lifts less than 1. This means that the products are negatively correlated and actually make someone less likely to buy another product.
|
A graphical example
|
Links to resources on Association Rules
Using Association Rules
Suggestive Selling - Identify the most likely to succeed up-sell and cross-sell opportunities.
- Provide your company's Customer Associates with weekly reports of what to up-sell and cross-sell.
Promotions - Identify complements and substitutes.
- Quantify how product combinations perform during promotions.
- Identify those products that you should NEVER promote at the same time.
Store Layout - Place products that sell well together to increase the probability of more "customer-product-combination interactions".
- Strategically layout your store to test segment perferences
Differential and Trend Analysis - Save historical data files on our server and compare how "customer-product-combination interactions" performed over any time period.
Customer Segmentation
According to the American Marketing Association the "basis of segmentation" begins with "a product specific factor that reflects" differences in customer preferences and purchase behavior. For retailers, customer preference analysis is vital to understanding what customers will want today and in the future. CustomerTool is specifically designed to help retailers uncover customer preferences quickly, accurately, and more affordably than any of our competitors. Many retailers find segmentation to be time consuming, complicated or too technical. CustomerTool works with standard POS transaction data to make segmentation easy, accurate and fun. CustomerTool's service is designed to identify customer perferences through the use of Association Rules. Association Rules are widely used in data mining to uncover patterns in customer behavior. Click here to learn more about Association Rules.
Read more about Segmentation
"The process begins with a basis of segmentation-a product-specific factor that reflects differences in customers' requirements or responsiveness to marketing variables (possibilities are purchase behavior, usage, benefits sought, intentions, preference, or loyalty). Segment descriptors are then chosen, based on their ability to identify segments, to account for variance in the segmentation basis, and to suggest competitive strategy implications (examples of descriptors are demographics, geography, psychographics, customer size, and industry). To be of strategic value, the resulting segments must be measurable, accessible, sufficiently different to justify a meaningful variation in strategy, substantial, and durable."
Marketing Power
|
|
